Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/09BAB813-E53D-463D-8330-D215FCFEE47E | - |
| Αναγνωριστικό | https://doi.org/10.26233/heallink.tuc.74271 | - |
| Γλώσσα | en | - |
| Μέγεθος | 131 pages | en |
| Τίτλος | Application of artificial intelligent methodology in medical diagnosis | en |
| Τίτλος | Εφαρμογή και αξιολόγηση μεθόδων τεχνητής νοημοσύνης σε προβλήματα ιατρικής διάγνωσης | el |
| Δημιουργός | Spiliotis Georgios | en |
| Δημιουργός | Σπηλιωτης Γεωργιος | el |
| Συντελεστής [Επιβλέπων Καθηγητής] | Zervakis Michail | en |
| Συντελεστής [Επιβλέπων Καθηγητής] | Ζερβακης Μιχαηλ | el |
| Συντελεστής [Μέλος Εξεταστικής Επιτροπής] | Kalaitzakis Konstantinos | en |
| Συντελεστής [Μέλος Εξεταστικής Επιτροπής] | Καλαϊτζακης Κωνσταντινος | el |
| Συντελεστής [Μέλος Εξεταστικής Επιτροπής] | Sergaki Eleftheria | en |
| Συντελεστής [Μέλος Εξεταστικής Επιτροπής] | Σεργακη Ελευθερια | el |
| Εκδότης | Πολυτεχνείο Κρήτης | el |
| Εκδότης | Technical University of Crete | en |
| Ακαδημαϊκή Μονάδα | Technical University of Crete::School of Electrical and Computer Engineering | en |
| Ακαδημαϊκή Μονάδα | Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών | el |
| Περίληψη | Magnetic Resonance Imaging (MRI) is a widely used medical imaging modality that provides accurate information about the human tissue, anatomy and pathology, of a non-invasive form. If used to scan a human brain it can provide images of high contrast, therefore distinguishing between the three major brain tissues: Cerebro-Spinal Fluid (CSF), Grey Matter (GM) and White Matter (WM). In this way MRI can greatly assist radiologists and doctors in providing a more precise diagnosis and therapy. Because of their unpredictable appearance and shape, segmenting brain tumors from multi-modal imaging data is one of the most challenging tasks in medical image analysis. Manual detection and classification of brain tumor by an expert is still considered the most acceptable method, but it is too time-consuming, especially because of the large amount of data that have to be analysed manually. In this thesis we examine, optimize and finally combine specific state-of-the-art methods comprising of four consistent methods for Computer-Aided Diagnosis (CAD) processes for detection of a brain tumor from MRI, of T2 weighted modality, from the axial plane (T2 MRI). We denote the four proposed methodologies as “Method 1” to “Method 4”. These methodologies are based on image pre-processing and classification by utilizing neural networks (NN) or a hybrid combination of neural networks and fuzzy logic (ANFIS). In order to gauge the current innovation status in automated brain tumor segmentation and to compare various proposed methods in bibliography, we use a large dataset of brain tumor MR scans, in which the relevant tumor structures have been delineated. These are provided freely from Multimodal Brain Tumor Image Segmentation (BRATS) MICCAI 2015. Our dataset of the training and testing data set, referred to male and female adult persons, includes 24 non-tumorous cases and 202 tumorous cases that all have been segmented visually by our experienced neurosurgeon partner, Dr. A. Krasoudakis. The healthy MRI scans are from “St. George” general hospital of Chania, Crete and from Harvard General Hospital database. Our data set contains about 5% high-grade, 82% low-grade glioma cases, 3% unhealthy but not recognizable cases and 10% healthy cases. At the pre-processing stage we apply a skull-stripping algorithm to isolate the brain region. Subsequently, we use a high-pass Gaussian filter for sharpening and a median filter for noise reduction. In post-processing stage, for image segmentation we use Otsu’s threshold as well as we implement morphological operators for region of interest (ROI) definition. In our proposed CAD Method 1, feature extraction is made using Grey-level co-occurrence matrix (GLCM) and 13 statistical features are calculated. In Method 2, feature extraction is made by using Discrete Wavelet Transform (DWT) and dimensionality reduction is implemented using Principal Components Analysis (PCA). In Method 3, all the above methods are combined and GLCM matrix is applied after the DWT and PCA stages, so to provide the necessary statistical features. In Method 4, the Mean-Shift algorithm is implemented at the post-processing stage for better segmentation results and features extraction is made according to Method 3. The features extracted from every proposed CAD method are processed at first with a feed-forward artificial neural network (ANN) with back-propagation training algorithm and then with an adaptive neuro-fuzzy inference system (ANFIS) for the methods with GLCM matrix. The experimental results of the proposed methods have been validated and evaluated for performance over a testing set of images based on sensitivity, specificity and accuracy with the best results reaching 98.8% sensitivity, 62.5% specificity and 95.6% accuracy. | en |
| Τύπος | Διπλωματική Εργασία | el |
| Τύπος | Diploma Work | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2018-04-25 | - |
| Ημερομηνία Δημοσίευσης | 2018 | - |
| Θεματική Κατηγορία | Image analysis | en |
| Θεματική Κατηγορία | Biostatistics | en |
| Θεματική Κατηγορία | Dimensionality reduction | en |
| Θεματική Κατηγορία | Feature extraction | en |
| Θεματική Κατηγορία | Data storage | en |
| Θεματική Κατηγορία | Τεχνητή νοημοσύνη | el |
| Θεματική Κατηγορία | Artificial intelligence | en |
| Βιβλιογραφική Αναφορά | Georgios Spiliotis, "Application of artificial intelligent methodology in medical diagnosis", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2018 | en |
| Βιβλιογραφική Αναφορά | Γεώργιος Σπηλιώτης, "Εφαρμογή και αξιολόγηση μεθόδων τεχνητής νοημοσύνης σε προβλήματα ιατρικής διάγνωσης", Διπλωματική Εργασία, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2018 | el |
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